Authors: Tianlang Chen, Charilaos Kanatsoulis, Jure Leskovec
Abstract: Predictive tasks on relational databases are critical in real-world
applications spanning e-commerce, healthcare, and social media. To address
these tasks effectively, Relational Deep Learning (RDL) encodes relational data
as graphs, enabling Graph Neural Networks (GNNs) to exploit relational
structures for improved predictions. However, existing heterogeneous GNNs often
overlook the intrinsic structural properties of relational databases, leading
to modeling inefficiencies. Here we introduce RelGNN, a novel GNN framework
specifically designed to capture the unique characteristics of relational
databases. At the core of our approach is the introduction of atomic routes,
which are sequences of nodes forming high-order tripartite structures. Building
upon these atomic routes, RelGNN designs new composite message passing
mechanisms between heterogeneous nodes, allowing direct single-hop interactions
between them. This approach avoids redundant aggregations and mitigates
information entanglement, ultimately leading to more efficient and accurate
predictive modeling. RelGNN is evaluated on 30 diverse real-world tasks from
RelBench (Fey et al., 2024), and consistently achieves state-of-the-art
accuracy with up to 25% improvement.
Source: http://arxiv.org/abs/2502.06784v1
